Image registration is a process employed in a variety of applications that require or benefit from establishing spatial correspondences between images. For example, registration algorithms may be used in medical imaging applications to align image data taken from a patient at different points in time, using different imaging parameters and/or to align adjacent slices in a three dimensional (3D) image data set. Image registration may also be used to correct for motion between successive sections during MRI or CT scans, for example, or to align images from different subjects for comparison in medical research such as fMRI studies. Various nonmedical applications may also benefit from the use of image registration to integrate data taken at different times or from different perspectives, for example in computer vision, remote sensing, and astrophotography.
Image registration systems typically designate one image as the reference image to which a second target image is to be aligned. The alignment may be carried out by attempting to match the intensity patterns of the entire target image to those of the reference image, or by identifying particular features within the two images that should correspond with one another. In feature-based registration, a feature of interest may be first identified, either manually or automatically, in both the reference and target images and then further processed to register or align the features. Alternatively, each image may be divided into patches roughly the size of the features being used for alignment. Patches of the target image may then be compared to the patches of the reference image in a search for correspondences between the two images.
Once one or more correspondences between the target and reference images have been determined, the image registration system may compute a transform that will bring those correspondences into alignment. Depending upon the application in which the image registration is performed, alignment of target and reference images may require rigid, affine, or non-rigid transforms. The use of non-rigid transforms may be particularly effective in medical imaging applications, in which deformation of the subject is common due to breathing, anatomical changes, etc. Microscopy applications, for example, may require registration techniques that can successfully align images in the face of rotation and shearing of tissue samples in a liquid medium, as well as potential tearing, folding, artifacts, etc.
Image registration may be particularly useful in applications such as neural imaging, in which image structures are complex and numerous, and deformation is intrinsic to at least some of the methods of image acquisition. However, the search for correspondences in image data of such complexity can be computationally costly. On the other hand, efforts to reduce the computational complexity involved in image registration often produces unsatisfactory results with respect to accuracy and/or robustness of the registration.